Automatic modulation recognition (AMR) is an integral function of electronic support (ES) systems in exploiting electromagnetic emissions, performing threat analysis, and managing electronic attack (EA) to construct effective jamming waveforms. The AMR also has applicability in civilian applications such as civilian spectrum monitoring and adaptive radio communication technologies.
Extensive prior art exists on automatic modulation recognition. An overview of existing techniques and their comparative analysis is given in Dobre, O. A.; Abdi, A.; Bar-Ness, Y.; Su, W., “Survey of automatic modulation classification techniques: classical approaches and new trends,” IET Communications, vol. 1, no. 2, pp. 137, 156, April 2007. More recent developments on extending AMR capabilities to newer signaling schemes such as multiple input multiple output (MIMO) systems such as in Miao Shi; Bar-Ness, Y.; Wei Su, “STC and BLAST MIMO Modulation Recognition,” IEEE GLOBECOM '07. Vol., no., pp. 3034, 3039, 26-30 Nov. 2007; improving AMR performance in low signal to noise rations, increasing modulation recognition reliability using distributed sensors such as by Xu, J. L.; Wei Su; MengChu Zhou, “Distributed Automatic Modulation Classification With Multiple Sensors,” IEEE Sensors Journal, vol. 10, no. 11, pp. 1779, 1785, November 2010; reducing the latency in making a decision as per Cardoso, C.; Castro, A. R.; Klautau, A., “An Efficient FPGA IP Core for Automatic Modulation Classification,” IEEE Embedded Systems Letters, vol. 5, no. 3, pp. 42-45, September 2013; and reducing computational complexity for efficient hardware implementations such as in Wei Su; Xu, J. L.; Meng Chu Zhou, “Real-time Modulation Classification Based on Maximum Likelihood,” IEEE Communications Letters, vol. 12, no. 11, pp. 801, 803, November 2008.
In each of these prior art references, and indeed in the prior art generally, modulation recognition relies on signal samples captured from contiguous observations, requiring a signal record that is collected without interruption in the acquisition, or data recording, process.
There are certain applications where data cannot be captured continuously; therefore gaps occur in between signal acquisitions. One application where modulation recognition has to rely on non-contiguous observations is responsive (reactive) jammers.
Conventional active radio-controlled improvised explosive device (RCIED) jammers continuously emit energy in predefined frequency bands as a precaution against trigger signals regardless of actual signal activities. Responsive jammers, on the other hand, scan the radio spectrum during look through windows and the available transmit energy is focused on currently relevant spectral areas.
Performing other activities in between jamming cycles creates further opportunities for more effective jamming: for example, knowledge of signal type can assist in allocating the most effective jamming waveform to the currently detected threat. Similarly, identification of a network type can be used to exploit vulnerabilities in a particular network for more effective jamming. Modulation recognition is an essential tool in identifying signals and network types.
Non-contiguous data frames are inevitable in several other applications of time divisive nature. For example, there is an emerging need to develop algorithms that will support interoperability requirements of various applications such as tactical communications (TC), electronic attack (EA), intelligence surveillance and reconnaissance (ISR) systems, and electronic support (ES) systems.
Signal samples captured over non-contiguous time frames contain abrupt amplitude and phase discontinuities at the frame transitions. Fourier transform generates distortions in the frequency spectrum when the transformed signals contain abrupt changes. These distortions in the spectrum affect the performance of preprocessing stages relying on frequency domain processing in an automatic modulation classifier such as frequency offset correction. Further, several modulation recognition features depend on spectral and phase characteristics of the signals and corrupted features degrade the performance of the classifier.